Knee Surgery, Sports Traumatology, Arthroscopy最新文献

The management of anterior cruciate ligament (ACL) ruptures is carried out differently in prevention, treatment and rehabilitation for athletes than for non-athletes. However, different approaches to managing ACL ruptures are also discussed in different sports. This narrative review includes first time the current situation of ACL management in football by experiences from the practical routine as well as from scientific reports. No other sport shows such high number of literature reports and sufficient scientific evidence in the management of ACL ruptures like football and this article provides a football specific overview in prevention, treatment and return to sports strategies. These scientific reports underline the surgical treatment of ACL ruptures in football player and show a high sustainability and return to sports rate. However, reports from football show that despite good knowledge, the implementation of sustainable prevention and rehabilitation is difficult and needs improvement.

Clinical Trial Registration

Considering that this manuscript is a narrative review, no clinical trial registration is neccesary.

Level of Evidence

Level V.

Purpose

The differentiation between traumatic and degenerative rotator cuff tears (RCTs remains a diagnostic challenge with significant implications for treatment planning. While magnetic resonance imaging (MRI) is standard practice, traditional radiological interpretation has shown limited reliability in distinguishing these etiologies. This study evaluates the potential of artificial intelligence (AI) models, specifically a multimodal vision transformer (ViT), to differentiate between traumatic and degenerative RCT.

Methods

In this retrospective, single-centre study, 99 shoulder MRIs were analysed from patients who underwent surgery at a specialised university shoulder unit between 2016 and 2019. The cohort was divided into training (n = 79) and validation (n = 20) sets. The traumatic group required a documented relevant trauma (excluding simple lifting injuries), previously asymptomatic shoulder and MRI within 3 months posttrauma. The degenerative group was of similar age and injured tendon, with patients presenting with at least 1 year of constant shoulder pain prior to imaging and no trauma history. The ViT was subsequently combined with demographic data to finalise in a multimodal ViT. Saliency maps are utilised as an explainability tool.

Results

The multimodal ViT model achieved an accuracy of 0.75 ± 0.08 with a recall of 0.8 ± 0.08, specificity of 0.71 ± 0.11 and a F1 score of 0.76 ± 0.1. The model maintained consistent performance across different patient subsets, demonstrating robust generalisation. Saliency maps do not show a consistent focus on the rotator cuff.

Conclusion

AI shows potential in supporting the challenging differentiation between traumatic and degenerative RCT on MRI. The achieved accuracy of 75% is particularly significant given the similar groups which presented a challenging diagnostic scenario. Saliency maps were utilised to ensure explainability, the given lack of consistent focus on rotator cuff tendons hints towards underappreciated aspects in the differentiation.

Level of Evidence

Not applicable.

Introduction

Deep learning (DL) models have achieved remarkable performance in musculoskeletal (MSK) medical imaging research, yet their clinical integration remains hindered by their black-box nature and the absence of reliable confidence measures. Uncertainty quantification (UQ) seeks to bridge this gap by providing each DL prediction with a calibrated estimate of uncertainty, thereby fostering clinician trust and safer deployment.

Methods

We conducted a targeted narrative review, performing expert-driven searches in PubMed, Scopus, and arXiv and mining references from relevant publications in MSK imaging utilizing UQ, and a thematic synthesis was used to derive a cohesive taxonomy of UQ methodologies.

Results

UQ approaches encompass multi-pass methods (e.g., test-time augmentation, Monte Carlo dropout, and model ensembling) that infer uncertainty from variability across repeated inferences; single-pass methods (e.g., conformal prediction, and evidential deep learning) that augment each individual prediction with uncertainty metrics; and other techniques that leverage auxiliary information, such as inter-rater variability, hidden-layer activations, or generative reconstruction errors, to estimate confidence. Applications in MSK imaging, include highlighting uncertain areas in cartilage segmentation and identifying uncertain predictions in joint implant design detections; downstream applications include enhanced clinical utility and more efficient data annotation pipelines.

Conclusion

Embedding UQ into DL workflows is essential for translating high-performance models into clinical practice. Future research should prioritize robust out-of-distribution handling, computational efficiency, and standardized evaluation metrics to accelerate the adoption of trustworthy AI in MSK medicine.

Level of Evidence

Not applicable.

Purpose

This systematic review aims to use European Society of Parenteral and Enteral Nutrition (ESPEN) terminology and diagnostic criteria to determine the prevalence of nutritional disorders in hip and knee arthroplasty.

Methods

A systematic review of Level 1–4 evidence was conducted as per the PRISMA statement and Cochrane handbook for Systematic Review (PROSPERO ID: CRD 42023360496). In March 2024, AMED, CENTRAL, EMBASE, MEDLINE, Scopus and Web of Science were searched. Articles were included if they defined and reported the prevalence of nutritional disorders in hip and knee arthroplasty populations. Exclusion criteria were subtrochanteric fracture, pathological fracture and <50 cases. The risk of bias in non-randomised studies of interventions and risk of bias 2 tools were used to assess bias. No pooled analyses were performed due to study heterogeneity.

Results

Fifty-five studies and 2,107,283 patients were included. Thirty-nine different definitions of nutritional disorder were identified. The prevalence of nutritional disorder varied depending on the chosen definition: 0.9%–71.7% in primary, 1.33%–47.5% in revision and 4.5%–60% in hip fracture arthroplasty. Thirty-four studies used albumin to diagnose malnutrition, with hypoalbuminaemia seen most frequently in hip fracture (20.3%–71.13%) and revision cohorts (2.5%–42.8%). No study reported the prevalence of sarcopenia in revision or hip fracture cohorts.

Conclusion

All forms of nutritional disorder exist within hip and knee arthroplasty populations, particularly among revision and hip fracture patients. Included studies showed poor compliance with ESPEN recommendations and heterogeneity in the chosen definition of disorder. A prospective study using ESPEN-recommended diagnostic criteria is required to better determine the prevalence of nutritional disorders, contributing towards the understanding of the financial and patient-related costs following hip and knee arthroplasty.

Level of Evidence

Systematic review of articles with Level I–IV evidence.

Purpose

The aim of this systematic review was to evaluate the outcomes and complications associated with technology-assisted revision total knee arthroplasty (revTKA).

Methods

A systematic search of PubMed, EMBASE, Web of Science and the Cochrane Library was conducted from inception to 31 October 2024. The inclusion criteria were experimental or observational studies with ≥10 patients undergoing robotic revTKA, evaluating clinical and/or radiological outcomes and/or complication rates. The exclusion criteria were isolated patellar revision, in vitro studies, letters to the editor, book chapters, conference papers, and studies without accessible full text. Each study was given a quality rating using the methodological index for non-randomised studies (MINORS). The included studies were divided into those reporting revision from TKA and those from unicompartmental knee arthroplasty (UKA), for both qualitative and quantitative synthesis. Random-effects meta-analyses were performed where appropriate. Mean differences with 95% confidence intervals (CIs) were calculated for radiographic parameters. Heterogeneity was assessed using the I² statistic.

Results

Across 20 studies (795 cases), 10 assessed revTKA and 10 examined revUKA. Of the revTKA studies, four were comparative cohorts, while among the revUKA studies, seven were comparative cohorts. Technology-assisted revTKA showed fewer outliers in hip–knee–ankle angle (13.3% [95% CI, 8.7%–19.0%] vs. 26.1% [95% CI, 16.3%–38.1%]), superior component positioning within ±3° for the lateral distal femoral angle (88.4% [95% CI, 83.2%–92.4%] vs. 79.7% [95% CI, 68.8%–87.5%]) and for the medial proximal tibial angle (91.2% [95% CI, 86.3%–94.6%] vs. 82.6% [95% CI, 72.0%–89.8%]), and better joint line restoration (79.5% vs. 58.3% within 4 mm). Procedures required an additional 15–24 min. Complication rates were comparable between groups. For UKA revisions, outcomes were generally similar between technology-assisted and conventional techniques, with mixed results on alignment accuracy and clinical scores.

Conclusion

Technology-assisted revTKA achieves optimal alignment parameters and reduces the occurrence of outliers compared with conventional techniques. However, these radiographic improvements do not consistently translate into enhanced clinical outcomes or reduced complication rates.